# Konstantin (Sber / GigaChat)

#entity

## Summary

Engineer on Sber's GigaChat development team, doing R&D on agents. Author of the talk arguing that **git-based [[skills-as-memory|skills]] are the new memory of AI agents**.

## Current Understanding

Konstantin brings the model-builder's / R&D lens. His core architectural claim: put tools + data + usage history in one git repo and let the [[harness]] decide what to load. He maps the field's evolution (tools → MCP → skills, then [[agentic-loops|agent loops]]) and demonstrates auto-improvement (a weak GigaChat going 1/89 → 11/89 on a benchmark over a weekend).

## Evidence

- Talk "Git-based skills — the new memory of AI agents": harness definition, two-stage skill loading, git rules + CI back-pressure, personal DNA/trip/HR skills, Hermes curator, Ralph/meta loops — [[2026-07-14-skills-based-on-git]].
- Hackathon record: 7th (Interpress-Ex, infinite-loop harness), 3rd/1st-technical (Snowbase), observed 19/20 top BitGen teams on harnesses.

## Related Pages

- Concepts: [[skills-as-memory]], [[harness]], [[evolution-of-agent-tooling]], [[agentic-loops]], [[context-as-scarce-resource]]
- Tools: [[hermes]] (skills-first harness he uses/extends), [[claude-code]]
- Timeline: [[ai-agent-evolution]]
- Compare: [[allie-miller]] (same skills-as-memory idea, business framing), [[theo-browne]] (orchestration era)
- Comparison: [[theo-konstantin-allie]] — three-lens side-by-side (Theo/Konstantin/Allie)

## Contradictions / Uncertainty

- Pruning windows (30/90 days) and the ">5 tool calls → make a skill" rule are presented as working heuristics from Hermes, not established standards. Status: tentative.
- No standards yet for what data to put in a skill or its upper size limit.

## Next Questions

- How to build world-models / give agents perception (his own "missing piece")?